Understanding the long-term evolution of hierarchical triple systems is challenging due to its inherent chaotic nature, and it requires computationally expensive simulations. Here we propose a convolutional neural network model to predict the stability of hierarchical triples by looking at their evolution during the first $5 \times 10^5$ inner binary orbits. We employ the regularized few-body code \textsc{tsunami} to simulate $5\times 10^6$ hierarchical triples, from which we generate a large training and test dataset. We develop twelve different network configurations that use different combinations of the triples' orbital elements and compare their performances. Our best model uses 6 time-series, namely, the semimajor axes ratio, the inner and outer eccentricities, the mutual inclination and the arguments of pericenter. This model achieves an area under the curve of over $95\%$ and informs of the relevant parameters to study triple systems stability. All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.

翻译：了解三层系统的长期演变具有内在的混乱性质,具有挑战性,它需要计算昂贵的模拟。在这里,我们提出一个进化神经网络模型,以预测三层的稳定性,通过观察它们在最初5美元时的演变情况来预测三层的稳定性。我们使用固定化的少数机体代码 \ textsc{tsunami} 模拟5美元乘以10 6美元三层的值,从中我们产生大量的培训和测试数据集。我们开发了12个不同的网络配置,使用三层元素的不同组合并比较其性能。我们的最佳模型使用6个时间序列,即半主轴比率、内心和外心、相互偏心和周围的论据。这个模型达到一个超过95 美元曲线的区域,并告知相关参数以研究三层系统稳定性。所有经过培训的模型都可供公开使用,可以预测等级三层系统的稳定性为200美元,比纯体值高出200美元的方法要快。